Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming
This work addresses the problem of building human-like AI systems with explainable AI for researchers in automated reasoning, but it is incremental as it applies an existing method (s(CASP)) to a new domain (Event Calculus).
The paper tackled the challenge of automating commonsense reasoning in Event Calculus by addressing difficulties with continuous change, constraints, and inference methods, proposing the use of s(CASP) to model and reason in domains with dense time and fluents, enabling deductive and abductive reasoning tasks.
Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.